How Airbnb, Huawei, And Microsoft Are Using AI and Machine Learning

Machine learning and Artificial Intelligence are two of the most important developments of the past 10 years within businesses. They have been at the core of the success of several companies, from Facebook’s advertising policies through to how American Airlines monitors wear of their plane engines.

We wanted to take a look at three companies who are doing some impressive work in the area who are often overlooked for their efforts, either because they are known for other areas or because their competitors sit in the data science limelight.

When you think machine learning, you don’t naturally go to ‘short term letting’. Renting out rooms and flats doesn’t seem like it would be an especially data-driven enterprise, but this couldn’t be further from the truth.

Airbnb have not only revolutionized the way that people book their accommodation when in new cities, they have also revolutionized the way the industry utilizes machine learning techniques. A big part of this came from their acquisition of Crashpadder in early 2012, which saw Dan Hill, co-founder of the company becoming product lead at Airbnb and implement Aerosolve, which has since been made open source and available to anybody who wants to use it.

This system creates a pricing algorithm that allows the company to set pricing based on the most popular elements of a property, from the most obvious, like location, through to the more obscure, like the way the photos are taken.

‘Here’s where the learning comes in. With knowledge about the success of its tips, our system began adjusting the weights it gives to the different characteristics about a listing—the “signals” it is getting about a particular property. We started out with some assumptions, such as that geographic location is hugely important but that usually the presence of a hot tub is less so. We’ve retained certain attributes of a listing considered by our previous pricing system, but we’ve added new ones. Some of the new signals, like ‘number of lead days before booking day,’ are related to our dynamic pricing capability. We added other signals simply because our analysis of historical data indicated that they matter. For instance, certain photos are more likely to lead to bookings. The general trend might surprise you—the photos of stylish, brightly lit living rooms that tend to be preferred by professional photographers don’t attract nearly as many potential guests as photos of cozy bedrooms decorated in warm colors. As time goes on, we expect constant automatic refinements of the weights of these signals to improve our price tips.’

However, it is not only in pricing that the company is utilizing this kind of technology, they are also using it to increase diversity within their teams, detection of fraudulent payments, identify host preferences and even model business impacts of potential product changes.